The proposed method is summarized in fig:modelsummary. We make several contributions
and important observations: 1) We propose a simple yet powerful COL that uses directional statistics
for modeling. Our COL module can be built on top of off-the-shelf pre-trained Faster-RCNN models without extra parameters.
We observe that using feature vector directions in our probabilistic model, we can
better capture the semantic information compared to
Gaussian distribution. To our knowledge, employing directional statistics for multiple instance learning is new.
2) We employ a detection module to extend COL to few-shot WSOD.
To the best of our knowledge few-shot WSOD not been studied in the literature before.
3) Despite its simplicity, our method outperforms sophisticated few-shot COL
algorithms [1]}, [2]} on PASCAL
VOC [3]}, MS COCO [4]}, and ILSVRC
detection [5]} benchmarks. In WSOD, our method outperforms
recent knowledge transfer based approaches [6]}, [7]}
in both few-shot and large-scale settings.
